Kelp vs Urchins
Nick Toimieri
sys.date()
1 Triggers and settings
2 Import Data
Note: for Neah Bay in 2016
- There are not algae data for the South Area only, the North.
- There are are only two transects of invert data for the North. There are 5 transects in the South area.
3 By KELP SPECIES but summed across all urchins
## `summarise()` has grouped output by 'site', 'year', 'zone', 'area', 'transect'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'site', 'year'. You can override using the `.groups` argument.
3.1 Individual plot version
The following splits the facet into individual plots for better plotting and labeling.
## `summarise()` has grouped output by 'site', 'year', 'zone', 'area', 'transect'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'site', 'year'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'site', 'year', 'zone', 'area', 'transect'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'site', 'year'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'site', 'year', 'zone', 'area', 'transect'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'site', 'year'. You can override using the `.groups` argument.## `geom_smooth()` using formula 'y ~ x'
3.2 Site x Year regressions for urchins vs kelp
##
## Call:
## lm(formula = Kelp ~ Urchins, data = nereo[nereo$site == "Tatoosh Island",
## ])
##
## Residuals:
## 1 2 3 4 5 6
## -0.9531 1.2689 0.2367 -0.3856 0.7625 -0.9294
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8447189 0.7712348 2.392 0.075 .
## Urchins 0.0007168 0.0151550 0.047 0.965
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.021 on 4 degrees of freedom
## Multiple R-squared: 0.000559, Adjusted R-squared: -0.2493
## F-statistic: 0.002237 on 1 and 4 DF, p-value: 0.9645##
## Call:
## lm(formula = Kelp ~ Urchins, data = nereo[nereo$site == "Destruction Island",
## ])
##
## Residuals:
## 1 2 3 4 5 6
## 0.18941 -0.19240 0.69272 0.05353 0.11054 -0.85380
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.20345 0.27457 4.383 0.0118 *
## Urchins -0.02019 0.02478 -0.815 0.4609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5694 on 4 degrees of freedom
## Multiple R-squared: 0.1423, Adjusted R-squared: -0.07208
## F-statistic: 0.6638 on 1 and 4 DF, p-value: 0.4609##
## Call:
## lm(formula = Kelp ~ Urchins, data = ptery[ptery$site == "Tatoosh Island",
## ])
##
## Residuals:
## 1 2 3 4 5 6
## 0.2926 -0.1347 -0.5921 0.2537 0.5546 -0.3740
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.410210 0.371301 1.105 0.3312
## Urchins 0.025339 0.007296 3.473 0.0255 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4915 on 4 degrees of freedom
## Multiple R-squared: 0.7509, Adjusted R-squared: 0.6887
## F-statistic: 12.06 on 1 and 4 DF, p-value: 0.02552##
## Call:
## lm(formula = Kelp ~ Urchins, data = ptery[ptery$site == "Destruction Island",
## ])
##
## Residuals:
## 1 2 3 4 5 6
## -0.26151 0.23442 -0.12751 -0.03642 -0.02639 0.21742
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.480561 0.104792 4.586 0.0101 *
## Urchins -0.003854 0.009456 -0.408 0.7045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2173 on 4 degrees of freedom
## Multiple R-squared: 0.03987, Adjusted R-squared: -0.2002
## F-statistic: 0.1661 on 1 and 4 DF, p-value: 0.70454 Total for each.
I know we’re not supposed to combine macro & nereo but…just to see
## `summarise()` has grouped output by 'site', 'year', 'zone', 'area'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'site'. You can override using the `.groups` argument.
5 BY SPECIES
5.1 Transect level, all depths
##
## Attaching package: 'cowplot'## The following object is masked from 'package:ggpubr':
##
## get_legend## Loading required package: viridisLite
## By Site and Depth level
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override using the `.groups` argument.## [1] 495 7## [1] 165 5## [1] 165 5
correlation purple vs nereo at Tatoosh r = 0.2273737, p = 0.0950256
5.2 By Site and 5 m only
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override using the `.groups` argument.## [1] 495 7## [1] 165 5## [1] 165 5## $x
## [1] "Urchin density"
##
## $y
## [1] "Kelp density"
##
## $colour
## [1] "Site"
##
## attr(,"class")
## [1] "labels"
5.3 By site, averaged across depths, data = years
## `summarise()` has grouped output by 'year', 'site'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site'. You can override using the `.groups` argument.## [1] 270 7## [1] 90 4## [1] 90 4
## [1] NA## [1] NAThis plot compared to the previous is interesting.
At the transect level, there is a negative correlation between urchin density and kelp neroycystis density at Tatoosh
At the site level, there is a positive correlation for Nerocystis (r = NA) and for Pterogophora (r = NA)at Tatoosh across years.
6 TATOOSH
6.1 By transect
##
## Formula: Y ~ a * exp(k * X)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## a 2.66810 0.51870 5.144 3.84e-06 ***
## k -0.11738 0.07926 -1.481 0.144
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.17 on 54 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 8.24e-06## a k
## 2.6681018 -0.1173773## [1] 249.6557
7 Across years
## `summarise()` has grouped output by 'year', 'site', 'area', 'zone'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
8 Coastwide urchin - kelp patterns
8.1 All kelp: Nereo + Macro + Ptero
## `summarise()` has grouped output by 'year', 'site', 'area', 'zone'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
cor: r = 0.6439593; p = 0.1675807
8.2 Canopy only
## `summarise()` has grouped output by 'year', 'site', 'area', 'zone'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
cor r = 0.3495239
8.3 Nero only
## `summarise()` has grouped output by 'year', 'site', 'area', 'zone'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
8.4 Macro only
## `summarise()` has grouped output by 'year', 'site', 'area', 'zone'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
## Ptero only
## `summarise()` has grouped output by 'year', 'site', 'area', 'zone'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
cor r = 0.8916147; = 0.0169844
9 Tatoosh only
## `summarise()` has grouped output by 'site', 'year', 'transect', 'area'. You can override using the `.groups` argument.##
## Formula: Y ~ a * exp(k * X)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## a 12.73286 2.03142 6.268 6.32e-08 ***
## k -0.01436 0.01149 -1.250 0.217
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.459 on 54 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 1.446e-06## a k
## 12.73285884 -0.01436079## [1] 414.5497
## `summarise()` has grouped output by 'site', 'year', 'transect', 'area'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'site', 'year', 'transect', 'area'. You can override using the `.groups` argument.##
## Formula: Y ~ a * exp(k * X)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## a 4.785827 1.035885 4.620 2.42e-05 ***
## k 0.004516 0.031080 0.145 0.885
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.728 on 54 degrees of freedom
##
## Number of iterations to convergence: 4
## Achieved convergence tolerance: 5.117e-07## a k
## 4.785826681 0.004515701## [1] 358.3601
10 Additional supplement figure
11 Figure 6 output for main MS
## `geom_smooth()` using formula 'y ~ x'
12 Combined Figure 5
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
13 Kelp vs Kelp
## `summarise()` has grouped output by 'site', 'year'. You can override using the `.groups` argument.
Correlations between kelps
Macro vs Nereocystis, all sites r = -0.4863198 with p = 0.0064318
Macro vs Nereocystis, two sites r = 0.2257643 with p = 0.4804755
Macro vs Pterygophora, all sites r = 0.2289692 with p = 0.2235754
Macro vs Nereocystis, all sites r = 0.1389036 with p = 0.464145
14 Correlations at Tatoosh Island across years (yearly means) for different depth zones
A different, and simplified version of the above for just tatoosh and faceted by species.
Essentially, there are different relationships at different depths. Probably too much detail for this manuscript.
## `summarise()` has grouped output by 'year', 'site', 'area', 'zone', 'transect'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'site', 'year', 'zone'. You can override using the `.groups` argument.